"""simple docstring"""



import random



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    a[left_index]
       __a       =    left_index + 1
       for j in range(left_index + 1   ,    a				):
              if a[j] < pivot:
                     __a ,   __a       =    a[i], a[j]
                     i += 1
       __a ,   __a       =    a[i - 1], a[left_index]
       return i - 1



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       if left < right:
              __a       =    random.randint(a   ,    right - 1				)
              __a ,   __a       =    (
                  a[left],
                  a[pivot],
              )  # switches the pivot with the left most bound
              __a       =    partition(a   ,    a   ,    a				)
              quick_sort_random(
                  a   ,    a   ,    a				)  # recursive quicksort to the left of the pivot point
              quick_sort_random(
                  a   ,    pivot_index + 1   ,    a				)  # recursive quicksort to the right of the pivot point



def 	_lowerCamelCase(	):
       __a       =    input("Enter numbers separated by a comma:\n"				).strip()
       __a       =    [int(a				) for item in user_input.split(","				)]

       quick_sort_random(a   ,    0   ,    len(a				)				)

       print(a				)


if __name__ == "__main__":
   main()

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
